{
  "abstract": "Plant resistance (R) genes, key players in the immune system, have traditionally been identified with HMM-based tools relying on sequence homology, whose sensitivity decreases for divergent or non-canonical resistance proteins. Here, DREM (Deep Resistance gene Evaluator Model) is presented as a deep learning framework that learns the compositional properties of resistance proteins from sequence alone rather than searching for predefined motifs. The framework combines two bidirectional long short-term memory networks with attention: a binary resistance classifier and a multi-label classifier of resistance domains. Validation on experimentally characterized proteins from RefPlantNLR recovered 99.8% of sequences, and on 15,000 held-out proteins DREM outperformed the deep learning predictor PRGminer (Matthews correlation coefficient 0.938 vs 0.820), with about two-thirds fewer classification errors. Across five plant proteomes, DREM identified 4,046 High Confidence or Candidate proteins; canonical domain architecture was confirmed in 75.5%, whereas 992 carried strong DREM signals without detectable Pfam domains, and 54 of 55 characterized nucleotide-binding leucine-rich repeat (NLR) loci ranked within the top 1.7% of their proteomes. Unlike domain-gated pipelines such as R-Predictor, DREM also recovered experimentally validated atypical NLRs, RPW8 proteins and non-receptor resistance genes. Attention did not concentrate on canonical ATP-binding motifs, consistent with the model drawing on sequence information beyond conserved domain signatures. Lightweight and applicable to complete proteomes, DREM complements domain-based annotation with a calibrated ranking. It links genome-wide prediction to the selection of resistance genes for functional validation, including atypical receptors missed by profile-based tools.",
  "abstract_shown": true,
  "affiliations": [
    "University of Naples \"FedericoII\""
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "R. Dublino"
    },
    {
      "name": "V. Carandente"
    },
    {
      "name": "G. Sperli"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Ercolano"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0zhpz740p1kwp.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.21.760626",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760626v1.full",
  "id": "0zhpz740p1kwp",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760626v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "plant biology"
    ],
    [
      "openalex",
      "1110|Plant-Microbe Interactions and Immunity"
    ]
  ],
  "openalex": "W7223171017",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760626v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0zhpz740p1kwp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:08Z",
      "native_id": "10.64898/2026.10.21.760626",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760626v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:plant biology",
      "subfield": "plant-biology"
    }
  ],
  "title": "DREM: integrating resistance and domain probabilities to prioritize plant immune receptors",
  "title_text": "DREM: integrating resistance and domain probabilities to prioritize plant immune receptors",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:08Z",
  "url": "https://research.draconitic.com/paper/0zhpz740p1kwp",
  "venue": "bioRxiv",
  "versions": []
}